Economic and financial modeling and planning is commonly used to estimate or predict the performance and outcome of real systems, given specific sets of input data of interest. An economic-based system will have many variables and influences which determine its behavior. A model is a mathematical expression or representation which predicts the outcome or behavior of the system under a variety of conditions. In one sense, it is relatively easy to review historical data, understand its past performance, and state with relative certainty that the system's past behavior was indeed driven by the historical data. A much more difficult task is to generate a mathematical model of the system which predicts how the system will behave with different sets of data and assumptions.
In its basic form, the economic model can be viewed as a predicted or anticipated outcome of a mathematical expression, as driven by a given set of input data and assumptions. The input data is processed through the mathematical expression representing either the expected or current behavior of the real system. The mathematical expression is formulated or derived from principles of probability and statistics, often by analyzing historical data and corresponding known outcomes, to achieve a best fit of the expected behavior of the system to other sets of data. In other words, the model should be able to predict the outcome or response of the system to a specific set of data being considered or proposed, within a level of confidence, or an acceptable level of uncertainty.
Economic modeling has many uses and applications. One area in which modeling has been applied is in the retail environment. Grocery stores, general merchandise stores, specialty shops, and other retail outlets face stiff competition for limited customers and business. Most if not all retail stores expend great effort to maximize sales, volume, revenue, and/or profit. Economic modeling can be a very effective tool in helping store owners and managers achieve these goals.
Economic modeling typically requires large amounts of data. In the retail environment, the data is collected at the completion of the transaction, usually during the check-out process. The transactional log (T-LOG) data contains information and attributes about the items purchased, time and date of purchase, store, price, promotions, customer attributes, and so on. The T-LOG data is stored in a database for use by the retailer in generating and using the models.
Statistical models inherently have some degree of inaccuracy or uncertainty in several dimensions, such as price elasticity, promotional lift, and aggregations. The uncertainty directly relates to the level of user confidence in the reliability of the model. The forecast generated by the model changes with the historical data, which is statistically noisy or random and can vary by product, store, time period, and customer traffic. Attempting to model unknown areas, i.e., where there exists little or no historical data, increases the uncertainty of the forecast. For example, multiple and widely varied price points in the sales history provide better estimates of price elasticity than few and narrowly separated price points.
One method of estimating the confidence interval on a forecast for a particular price point where no historical data exists involves evaluating historical data for prices that do exist and then projecting that basis to the unknown pricing of interest, e.g., by linear interpolation. Alternatively, the forecast can use a simple empirical measure of forecast error. In such an approach, the distribution of the error in the model to historical sales is assumed to be uncorrelated with the time dependent application of causal factors. However, this approach does not account for future changes in key retail factors like price changes and promotional offers. Therefore, confidence bands could be too high or too low depending on the scenario as described by the offer price, promotional activity, advertising dollars spent, etc. Such estimates are notoriously inaccurate in their own right and do not yield the desired confidence or understanding of the true accuracy of the prediction. In order to understand and make full use of the forecast, the user needs a complete and accurate story of what the model is actually conveying.